Scientists recently developed a flow direction model which produces high-quality flow routing parameters on any mesh system.

Researchers designed ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.

Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.

Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

PNNL chemical engineer Reid Peterson helped develop the process to pretreat Hanford Site tank waste by removing cesium-137.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

From air-sealing windows and checking for leaky ducts to insulating the attic, PNNL researchers offer tips on how to keep a home warm in winter weather.

Battery energy storage systems are being proposed in municipalities across the U.S. PNNL researchers can help community planners guide safe siting and operations.

A novel electrode functionalization strategy enables efficient removal of toxic heavy metals from wastewater.

1-propanol converts from a monomer into a trimer at aluminum sites within zeolite pores.

Investigating the impact of global warming on mesoscale convective systems initiation and growth with a theoretical model.

Understanding the contrasting response in relative humidity at ocean and land surfaces under global warming.

The cross-equatorial surge enhances the southward development of the Madden–Julian oscillation because of increased moisture convergence.

Ripples demonstration will take place at the DOE booth at the International Conference for High Performance Computing, Networking, Storage, and Analysis.

PNNL scientists took thousands of measurements of firefighters in training to learn more about how the body responds to vigorous exercise.

The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.

To identify communities ready for marine energy, help them realize their energy resilience goals, and facilitate community leadership in future projects, two national laboratories are developing the Deployment Readiness Framework.

The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.